AI Security
AI Security & Testing
Detect and prevent attacks on AI systems. We test your AI models for robustness, attack vectors, and undesirable behaviours.
AI-Specific Attack Vectors
AI systems are vulnerable to attacks that differ fundamentally from classical IT attacks. Adversarial attacks manipulate inputs to produce incorrect outputs. Data poisoning corrupts training data to alter model behaviour over time. Model inversion can reconstruct sensitive training data. We identify and remediate these risks through structured security assessment.
Adversarial Testing and Red Teaming
Our red team simulates realistic attacks on your AI systems using established frameworks including MITRE ATLAS and the OWASP Top 10 for LLMs. We test both technical robustness and conceptual weaknesses in AI design – covering the full attack surface from model inputs to system integration.
Fairness and Bias Analysis
Discrimination by AI systems is not only an ethical concern but an increasingly regulatory risk. We analyse your models for undesirable bias effects and support the development of debiasing strategies – in alignment with the requirements of the EU AI Act and sector-specific obligations.
Why Conventional Penetration Tests Do Not Cover AI Gaps
Conventional penetration tests assess networks, application layers, and access controls – not the specific vulnerabilities of AI models. A web application penetration test cannot detect adversarial examples that deceive a computer vision model. AI security testing requires its own methods, frameworks, and expertise: MITRE ATLAS for AI attack tactics, OWASP ML Top 10 for model attacks, and specific test procedures for LLMs under OWASP LLM Top 10. Organisations deploying AI in production need both test layers.
Typical Attack Scenarios and Our Response
Insurance company with AI in claims processing
An insurer uses AI for automatic assessment of damage images. Adversarial attacks can inject manipulated images that lead the model to incorrect payout decisions. We test the robustness of the model and support the development of detection and defence mechanisms.
AI chatbot with prompt injection risk
A company operates an LLM-based customer service chatbot with access to internal knowledge bases. Prompt injection can cause the chatbot to disclose confidential information or perform unintended actions. We test and harden the LLM deployment against OWASP LLM Top 10.
AI in medical diagnostics with fairness requirements
A healthcare provider uses AI for imaging diagnostics. Systematic bias in the training dataset can disadvantage certain patient groups – with medical and legal consequences. We conduct fairness analyses and develop debiasing strategies in accordance with the EU AI Act.
Testing Portfolio
- Adversarial Robustness Testing
- Data Poisoning Assessment
- Model Inversion & Extraction Tests
- Prompt Injection Tests (LLMs)
- Bias & Fairness Analysis
- Red Teaming for AI Systems
- Privacy Risk Assessment
- MITRE ATLAS-based Evaluation
Frequently Asked Questions about AI Security Testing
What is the difference between AI testing and conventional penetration testing?
A conventional pentest assesses networks, applications, and access controls. AI testing assesses the robustness of the model itself: adversarial examples, data poisoning, model extraction, and prompt injection. Both test types complement each other – neither replaces the other.
Which AI systems should be tested?
Priority targets: AI systems that make security-relevant decisions (access controls, anomaly detection), high-risk AI under the EU AI Act, and any LLM-based systems with access to internal data or systems.
How often should AI testing be conducted?
After every significant change to the model or training data, after significant changes to the environment, and at least annually for production AI systems. For high-risk AI under the EU AI Act, regular testing is part of the mandated risk management system.
Can you test models we have purchased (not self-developed)?
Yes. Black-box testing is possible without access to source code or training details. We test model behaviour through the production interface and identify real attack vectors in the process.
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Put Your AI Systems to the Test
Find out whether your AI systems meet the requirements of modern security standards.